Modeling of biomass supply system by combining computational methods – A review article

Mika Aalto, Raghu Kc, Olli-Jussi Korpinen, Kalle Karttunen and Tapio Ranta

Applied Energy, 2019, vol. 243, issue C, 145-154

Abstract: As computing power increases, more complex computational models are utilized for biomass supply system studies. The paper describes three commonly used modeling methods in this context, geographic information systems, life-cycle assessment, and discrete-time simulation and presents bibliometric analysis of work using these three study methods. Of the 498 publications identified in searches of the Scopus and Web of Science databases, 17 reported on combinations of methods: 10 on life-cycle assessment and geographic information systems, six on joint use of life-cycle assessment and discrete-time simulation, and one on use of geographic information systems jointly with discrete-time simulation. While no articles dealt directly with simultaneous use of all three methods, several acknowledged the potential of this. The authors discuss numerous challenges identified in the review that arise in combining methods, among them computational load, the increasing number of assumptions, guaranteeing coherence between the models used, and the large quantities of data required. Discussion of issues such as the complexity of reporting and the need for standard procedures and terms becomes more critical as repositories bring together research materials, including entire models, from various sources. Efforts to mitigate many of modeling’s challenges have involved phase-specific modeling and use of such methods as expressions or uncertainty analysis in place of a complex secondary model. The authors conclude that combining modeling methods offer considerable potential for taking more variables into account; improving the results; and benefiting researchers, decision–makers, and operation managers by producing more reliable information.

Keywords: Biomass; Supply chain; Life cycle assessment; Geographical information system; Agent-based modeling and simulation; Discrete-event simulation (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (6)

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DOI: 10.1016/j.apenergy.2019.03.201

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